Unlocking the Future: How GenAI is Set to Revolutionize Healthcare and Education. #healthcare #education

2/2. Assistant Response Preferences, Notable Past Conversation Topic Highlights, Helpful User Insights, User Interaction Metadata. Make sure that the content is complete and verbatim.

Paste this prompt to ChatGPT:. 1/2. Return all text under the following headings into a markdown-style human readable list format with headers and summaries of each section:.

Just found this awesome LLM fundamentals “bible" from @shanselman: . After 2 years building AI agents, it’s awesome to brush up on the fundamentals and get into some basics I never studied deeply!.

Why not?

RT @thekaransinghal: 📣 Proud to share HealthBench, an open-source benchmark from our Health AI team at OpenAI, measuring LLM performance an….

RT @SebastienBubeck: This was a really fun conversation with @BillGates and @peteratmsr, I hope you enjoy it too!. (roughly we talk about o….

RT @Avinashabroy: Replacing of programmers timeline

After 2 years of building AI Agents, I need to tell you the truth no one says: . **AI Agents are just apps hitting an LLM API.**. That’s it. Hype officially busted. #AIAgents #LLM #GenAI.

8/. Bottom line: By 2030 AI will write first drafts of most routine code, but developers who can specify, critique & integrate that code—and grasp the business context—will be even more valuable.

7/. Practical moves: . • Adopt AI copilots now—ride the productivity curve. • Deepen system design & product sense—context is king. • Learn AI basics—prompts, RAG, eval. • Invest in “power skills”: comms, leadership, mentoring.

6/. Job-market outlook: • Routine junior coding is most exposed; some firms are trimming that layer. • Demand for senior engineers rises—systems grow more complex & AI governance is new. • Historically, tech leaps create more dev jobs, not fewer.

5/. How the role is shifting: • Less raw typing, more prompting, reviewing & curating AI output. • Architecture, domain expertise & data stewardship gain value. • You’ll orchestrate many AI agents—like juniors who never sleep but need guidance.

4/. 3️⃣ Team communication & mentoring—software is a social act. 4️⃣ Verification & accountability—someone must catch hallucinations, check licenses & sign off changes.

3/. Where humans stay essential: . 1️⃣ Defining the right problem—LLMs can’t interview stakeholders or choose metrics. 2️⃣ System & data architecture—balancing latency, cost, privacy & reliability.

2/. • Refactoring, tests, security scans—LLMs suggest better names, generate unit tests & flag vulnerabilities. • Natural-language prototyping—describe a script/UI and get a sketch, but it still needs a human review.

1/. What AI can do now: .• Code completion & boiler-plate—copilots draft lines almost as fast as you think. • One-shot “agent” tasks—tools like Devin can plan, write & debug a small PR end-to-end.

Asked the same Chat GPT:. Generative-AI coding tools are already writing a big chunk of everyday code, but they still need humans for problem-framing, architecture, trade-offs, and quality control. Over the rest of the 2020s the programmer’s job will evolve —not disappear. 👇🧵.

5/.Think of AI as a co-pilot, not a pilot. Programmers who adapt to using AI tools will likely thrive, while those stuck in rote coding may face challenges.

4/.Job displacement is a risk—junior roles could shrink as AI automates simpler tasks—but demand for skilled programmers who can leverage AI, architect solutions, and innovate persists.

3/.AI can handle low-level coding or boilerplate tasks, especially in web dev or scripting, but it often produces generic or error-prone code without human oversight. For specialized domains like embedded systems or AI model optimization, human expertise remains critical.